A new model based on specific characteristics of the donor
and the recipient may help predict survival after liver
transplantation, according to a new study. Published in the
November 2006 issue of Liver Transplantation, the official
journal of the American Association for the Study of Liver
Diseases (AASLD) and the International Liver Transplantation
Society (ILTS), the author writes, “This model could be used
to inform liver transplant candidates and their doctors what
post-transplant survival would be expected when a given
donor is offered and may be particularly helpful in marginal
or high risk donors.” The journal is published by John Wiley
& Sons, Inc., and is available online via Wiley InterSciencehttp://www.interscience.wiley.com/journal/livertransplantation.

Currently, nearly 18 thousand patients are awaiting liver
transplants, but because organs are scarce, only about 6,000
are transplanted each year. There are no universally
accepted criteria for liver donors. In addition, the
importance of various recipient characteristics to post-
transplant survival isn’t fully understood.

George Ioannou, M.D., M.S., of the Veterans Affairs Puget
Sound Health Care System in Seattle sought to identify donor
and recipient characteristics that are important predictors
of graft survival following liver transplantation. He then
used these predictors to develop and validate a survival
model.

Using information provided by the United Network for Organ
Sharing, Ioannou identified all patients who had a liver
transplant between 1994 and 2003. For his study, he did not
include patients who had donors under age 10 or over age 75,
living donors, split-liver donors, non-heart-beating donors,
or donors with serum sodium concentration greater than 170
mmoles/L. He also excluded patients with multiple organ
transplants, previous liver transplants and incomplete
information.

For the 20,301 patients who remained, including 6,477 with
hepatitis C (HCV), he used statistical models to examine the
relationship between donor and recipient characteristics and
survival after transplant. He then created two models that
predict survival after liver transplant – one for patients
without HCV and one for those with HCV. He validated the
models using data from patients not included in their
derivation.

Ioannou found that the donor age, cold ischemia time,
recipient MELD score, and cause of liver disease have the
greatest impact on survival. However, the best model for
patients without HCV included donor age, cold ischemia time,
gender, race/ethnicity, recipient age, BMI, MELD score,
status at time of transplantation, diabetes mellitus, cause
of liver disease, and serum albumin. For patients with HCV,
the best model included the same donor characteristics, and
all recipient characteristics except cause of liver disease
and serum albumin.

“Ultimately,” Ioannou writes, “risk scores and predicted
survivals determined from such models may be an objective
way to assess the risk of a given liver donor, recipient, or
donor/recipient combination.” Such models could improve the
fairness of organ distribution. For example, he suggests,
“if two donors are expected to be available at approximately
the same time, it would be more equitable for the recipient
with worse predicted post-transplant survival to receive the
donor with the better predicted survival and vice versa
since that would make the post transplant survival of the
two recipients more similar.”
An accompanying editorial by Ignazio R. Marino, M.D.,
F.A.C.S of the Thomas Jefferson University Hospital in
Philadelphia, says Ioannou’s study is an excellent starting
point for the debate about which patients receive the
limited supply of organs. He recommends a large prospective
study of liver transplant candidates to help optimize
allocation criteria and define when a prospective donor
should not be used for a prospective recipient. “We might
not be ready to match donor and recipient yet,” Marino
writes, “but this should be our ultimate goal.”